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library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.3.2
## Warning: package 'forcats' was built under R version 4.3.2
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#below code may be a bad practice

ggplot(data=faithful, 
       mapping=aes(x=eruptions,
                   y=waiting)) +
geom_point()

ggplot(data=faithful, 
       mapping=aes(x=eruptions,
                   y=waiting), shape='square') +
geom_point()

#Pro-practice

ggplot(data=faithful)+
       geom_point(aes(x=eruptions,
                   y=waiting,
                   color=eruptions>3))

ggplot(data=mtcars)+
    geom_point(aes(x=cyl,
                    y=disp,
                    color=cyl>4))

ggplot(data=mtcars)+
    geom_point(aes(x=hp,
                    y=cyl,
                    color=hp<65))

ggplot(data=diamonds)+
    geom_point(aes(x=color,
                    y=depth,
                    color=depth>70))

ggplot(data=faithful)+
       geom_point(aes(x=eruptions,
                   y=waiting))+
       geom_abline(slope=-40,intercept=200)

There are many types of Geoms

Their mapping requirements differ

#Histogram-for histgrams we need only 1 continuous variable for mapping x=…

ggplot(faithful)+
  geom_histogram(aes(x=eruptions))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(faithful)+
  geom_histogram(aes(x=eruptions, color=eruptions<3.1))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(faithful)+
  geom_histogram(aes(x=eruptions, fill=eruptions<3.1))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

‘Waiting’ Stacked behind-Transparent [may not be useful]

ggplot(faithful)+
  geom_histogram(aes(x=eruptions, fill=waiting<60), position = 'identity',alpha=0.5)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(faithful)+
  geom_histogram(aes(x=eruptions, fill=waiting<60), position = 'dodge',alpha=0.5)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mtcars)+
    geom_histogram(aes(x=cyl, fill=gear<4), position = 'dodge',alpha=0.5)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mtcars)+
 geom_histogram(aes(x=hp))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(mtcars)+
 geom_histogram(aes(x=cyl))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(faithful,
  aes(x=eruptions,y=waiting))+
  geom_density_2d()+
  geom_point()

ggplot(faithful,
  aes(x=eruptions,y=waiting))+
  geom_density2d_filled()+
  geom_point()

#Bar Plots

ggplot(mpg)+
  geom_bar(aes(x=class))

ggplot(diamonds)+
 geom_bar(aes(x=cut)) 

ggplot(diamonds)+
 stat_count(aes(x=cut)) 

ggplot(mpg)+
  stat_count(aes(x=manufacturer)) 

ggplot(mpg)+
  stat_count(aes(x=class), geom='point'
  )

Mean-in color Red

ggplot(mpg)+
  geom_jitter(aes(x=class, y=hwy), width=0.2)+
  stat_summary(aes(x=class, y=hwy), fun=mean, geom='point', colour='red', size=4)

ggplot(mpg)+
  geom_jitter(aes(x=class, y=hwy), width=0.2)+
  stat_summary(aes(x=class, y=hwy), fun=median, geom='point', colour='red', size=4)

#Not a great plot

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class))

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class))+
  scale_color_brewer(type='qual')

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class))+
  scale_color_brewer(name='CLASS_ravi', type='qual')

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class))+
  scale_x_continuous(breaks=c(3,5,6))+
  scale_y_continuous(trans='log10')

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class))+
  scale_color_brewer(palette = 'Set1')

#buBBLE CHART

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class, size=cyl))+
  scale_color_brewer(type = 'qual')

#Now legend is correctly reflecting

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class, size=cyl))+
  scale_color_brewer(type = 'qual')+
  scale_size(breaks=c(4,5,6,8))

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=class, size=cyl))+
  guides(colour='legend')

#Interesting

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy, color=cty, size=cty))+
  guides(colour='legend')

Facets-used for avoiding the overplotting

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy))+
  facet_wrap(~class)

Facets -doing graphics pivots

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy))+
  facet_grid(~drv)

each scale has their y-axis-It does not makes sense when comparing with 3 diff panesl

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy))+
  facet_wrap(~drv, scales = 'free')

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy))+
  facet_wrap(~drv, scales = 'free_x')

Not so effective way of shaowing it

ggplot(mpg)+
  geom_point(aes(x=displ, y=hwy))+
  facet_wrap(~drv+year)

ggplot(mpg)+
  geom_bar(aes(x=class))+
  scale_y_continuous(limits = c(0,40))
## Warning: Removed 3 rows containing missing values (`geom_bar()`).

above one with different code

library(tidyverse)
ggplot(mpg)+
  geom_bar(aes(x=class))+
  coord_cartesian(ylim = c(0,40))

#GGANIMATE-Story Telling and attention grabbing #1-Transitioning through time

# install.packages("gganimate")
library(gapminder)
## Warning: package 'gapminder' was built under R version 4.3.2
library(gganimate)
## Warning: package 'gganimate' was built under R version 4.3.2
library(gifski)
## Warning: package 'gifski' was built under R version 4.3.2
## standard ggplot2
myPlot <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

animate(myPlot, duration = 10, fps = 30, width = 750, height = 750, renderer = gifski_renderer())

anim_save("output.gif")
library(tidyverse)
library(gganimate)
library(gapminder)

g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

animate(g, height=400, width=600, renderer=gifski_renderer())

anim_save("gapminder.gif", g)

```{# {r} # amim_test$Year %>% # unique() # # scatter_anim <- amim_test %>% # ggplot(aes(x=Year, y=Sale # ))+ # geom_point(aes(color=Brand, # size=Sale))+ # ggrepel::geom_label_repel(aes(label=Sale), # nudge_x = -0.2, # alpha=0.7, # size=6)+ # theme_bw(base_size = 20) # # animate_scatter <- scatter_anim+ # labs(title = “Test”, # subtitle = “Year:{frame_time}”, # caption = “Test 2”)+ # transition_time(Year)+ # ease_aes(‘linear’) # animate(animate_scatter, # duration=9, # fps=1, # end_pause = 2, # width=1000, # height=800)

```